Separate multiple speaker in mono audio files
Efficent separation for noisy medical condition.
In May 2025, we’ll expose our multilingual diarization service outperforming most competitors. Trained on multiple domains including medical, noisy phone calls and various recording conditions providing industry utilisation.
Changelog
We are excited to unveil Monster, our new speaker diarization system, Nijta’s latest innovation in audio segmentation that redefines both accuracy and efficiency. This first release marks a major step forward in speaker identification, offering precise segmentation, multilingual support, and robust performance on your noisy data. From medical conversations to customer service calls and teleconference, our advanced model adapts to various acoustic conditions, ensuring high accuracy where other diarization systems fall short.
Features
- Precise speaker identification
- Seamless handling of overlapping speech
- Robust performance across varied acoustic environments
- Unlimited number of speakers